Adoption Of Cars Research Articles

Analysis Of Factors Influencing Consumer Purchase Intention For Electric Cars: A Case Study In Greater Jakarta

The demand for electric cars has significantly increased in recent years, but it represents only a small fraction of the total new vehicles sold globally. This study aims to analyze the factors influencing consumer purchase intentions towards electric cars in the Greater Jakarta area (Jabodetabek). Using a survey method, data were collected from individuals who are considered capable of purchasing electric cars, either through installment plans or cash payments, and analyzed using partial least squares structural equation modeling (PLS-SEM) to test the research hypotheses. The results indicate that government incentives, ease of access to charging infrastructure, perception of resale value, product diversification, social influence, environmental concern, and consumer attitudes significantly and positively influence purchase intentions towards electric cars. However, the perception of purchase price did not show a significant effect. The findings suggest that strategic initiatives by policymakers and manufacturers, such as continuous government incentives, expansion of charging infrastructure, and effective marketing strategies leveraging social influence and environmental benefits, are crucial for promoting electric car adoption in Jabodetabek. This study provides valuable insights for understanding consumer behavior towards electric cars in emerging markets and offers useful information for enhancing market penetration.

Adoption of Chinese cars and the shift to electric vehicles: Early evidence from Norway

Chinese auto manufacturers have started exporting electric cars to developed markets. I study data from the world’s leading market for electric vehicles, Norway, to provide some early descriptive evidence on the patterns of Chinese-brand new car sales. Using a Bayesian multilevel regression model, I find evidence of local peer effects: A higher proportion of sales of a Chinese branded car in a postal code increases the probability of a new car sale being Chinese. I also hypothesize that municipalities with high penetrations of electric cars will be associated with higher penetrations of Chinese-branded new car sales. I do not find evidence for this.

Sustainable Transportation Solutions for Intelligent Mobility: A Focus on Renewable Energy and Technological Advancements for Electric Vehicles (EVs) and Flying Cars

(1) Introduction: Transportation logistics play a pivotal role in facilitating both individual mobility and supply chain operations on a global scale. However, conventional transportation systems have contributed significantly to urban congestion and environmental degradation. In response to these challenges, there is growing momentum to investigate the potential of renewable energy to power electric vehicles (EVs) and flying automobiles, aiming to reduce fossil fuel dependence and carbon emissions. (2) Objectives: By analyzing key drivers and opportunities for integrating flying vehicles into existing infrastructure through dynamic modeling, this study seeks to accelerate the transition to sustainable transportation. (3) Methodology: A mixed-method approach, combining literature review and causal loop diagram analysis, is employed to understand the factors influencing EV and flying car adoption. (4) Results: Findings highlight the significant potential of renewable energy in reducing emissions and reliance on fossil fuels through widespread EV and flying vehicle adoption. The major drivers and challenges in infrastructure, safety, and airspace management are also identified. In addition, this research emphasizes the importance of sustainable transportation in addressing urban congestion, pollution, and energy security. (5) Conclusions: By leveraging renewable energy and embracing technological advancements, a low-carbon future for transportation can be achieved, benefiting both the environment and society.

Trust towards autonomous driving, worthwhile travel time, and new mobility business opportunities

ABSTRACT When driving autonomous cars, individuals have the opportunity to engage in non-driving activities, resulting in improved well-being. By incorporating trust into the technology acceptance modelling framework, this study extends the understanding of behavioural intention from the adoption of autonomous cars to the allocation of worthwhile travel time. Within this extended framework, the structural equation model is employed to identify significant paths to time allocation behaviour. Specifically, trust is identified as a crucial determinant of such behavioural intention. Then, we use the hierarchical ordered probit model to analyse the extent of such time allocation, accounting for scaling heterogeneity in self-reported responses. Our empirical evidence demonstrates the vital role of trust in encouraging the allocation of worthwhile and productive travel time. Based on these results, we present various new mobility business opportunities as well as some policy implications from the perspective of trust.

Exploring Electric Car Adoption Intent: Role of Current Travel Characteristics Among Daily Car Users

Exploring Electric Car Adoption Intent: Role of Current Travel Characteristics Among Daily Car Users

Corrigendum to: Electrifying for Sustainability’- Exploring Electric Car Adoption in the Indian Landscape

Corrigendum to: Electrifying for Sustainability’- Exploring Electric Car Adoption in the Indian Landscape

Car choice determinants in Italy and Norway: A comparison based on revealed and stated choices

Norway is the leading country in electric car adoption in the world, while in Italy electric cars are only recently gaining acceptance. We compared car choices in the two countries highlighting commonalities and differences in the choice determinants, distinguishing between the small and the large car segment. We analyzed actual choices made in the real-world conditions and stated choices under hypothetical scenarios. The comparison between the preference structures of the two countries shows important differences when the revealed preference dataset is analyzed, while the differences are much reduced with the stated preference dataset. All in all, we feel that the two countries present only differences associated with longer car driving habits of the Norwegian drivers, the higher percentage of large cars in Norway, and the more developed public charging infrastructure. Since the supply of cars is quite similar, such a consideration leads us to believe that the huge discrepancy in electric car uptake is mainly due to the different car policies adopted in the two countries. The evolution of the policy setting and of the technology will determine whether Italy will follow the Norwegian model of gradual BEV uptake.

EE51 A Cost-Consequence Analysis of Adopting Chimeric Antigen Receptor T-Cell Therapy for Patients with Relapsed or Refractory Large B-Cell Lymphoma in Saudi Arabia: a Multi-Center Comparison

There is currently only 1 referral center in Saudi Arabia for patients with relapsed or refractory (r/r) large B-cell lymphoma (LBCL) prescribed chimeric antigen receptor T-cell (CAR T) therapy. In this study we assessed the potential economic consequences of broader adoption of CAR T treatment at tertiary hospitals versus referring patients for treatment either in-country or abroad.

Understanding driverless car adoption: Random parameters ordered probit model for Brisbane, Melbourne and Sydney

With advanced policy and technology support, driverless cars are expected to be a reality on Australian roads in next 5–10 years. The pace at which driverless cars emerge as a mainstream travel mode greatly depends on public adoption and acceptance; this is likely to be based on multiple factors. The impact these factors may have on various adoption time horizons, however, is rarely discussed in contemporary research. Therefore, this study focuses on illustrating the impact of factors associated with the adoption of driverless cars in Australia. A survey was conducted on 2608 participants living in metropolitan Brisbane, Melbourne, and Sydney. A total of 1108 participants expressed their preference towards driverless car adoption over three different time intervals, ranging from now to 10 years following deployment. Four random parameters ordered probit models were developed using the survey data—three separate models for Brisbane, Melbourne, and Sydney, and one for the combined dataset. Significance differences among models were evaluated using the likelihood ratio test. The results showed a significant difference among cities in terms of contributing factors towards driverless car adoption timeline. Additionally, different groups, considering the following factors—gender, age, household members, level of education, employment status, income, driving licence duration, travel distance to school, travel distance for social outings, car ownership, familiarity with driving aids and driverless car riding—exhibited a mixed interest regarding the adoption timeline for driverless cars. Other groups, considering the following factors exhibited a unidirectional interest—age, level of education, workplace type, income, employment status, travel distance to work, travel distance to school, travel distance for social outings, number of employed in a household, occupation, driver's licence age, number of smart tech use, driverless car trip purpose, feature preference in a driverless car, and use of driving aids. The study offers insights for future planning and policy direction related to driverless car adoption.

Users’ Propensity to Use Self-Driving Systems of SAE Automation Level 1 and 2 Cars: Results of an Italian Survey

The automotive sector is currently developing advanced autonomous functionalities which are expected to be soon integrated into the vehicles. These vehicles can help to reduce road accidents, ease traffic congestion, improve fuel consumption, and reduce pollutant emissions. By contrast, there are still technological, normative, ethical, and social obstacles to the widespread adoption of self-driving cars, among which users’ acceptance covers a relevant issue. The aim of the paper was to investigate the users’ propensity to use self-driving systems of SAE automation Levels 1 and 2. To do this, an hoc mobility survey was performed in Italy among car drivers, investigating both the presence of these autonomous devices on board the vehicles currently used and their frequency of usage. Survey results show that 41% of the respondents currently have a Level 1 and/or 2 system on-board their car: 54% have only the Cruise Control (Level 1 car), while 46% have both of them (Level 2 car). Furthermore, about 85% of the respondent frequently (medium-high) use the Cruise Control and/or Lane Keeping Assist. More than 86% of the drivers stated that these devices significantly improve both road safety and driving stress (improve the travel experience). The highways are the roads where these self-driving systems are mainly used (more than 70% of the time). These results underline the relevant effort that the automotive industry has performed in the last decades about self-driving. In the last five years within the Italian market was observed an increase of more than 200% of the car standard equipment (no optional) with SAE automation Level 1 or 2 systems.

The Influence of Cultural Factors on Choosing Low-Emission Passenger Cars

The decrease in greenhouse gas emissions by passenger cars is one of the key factors for climate protection measures. Besides EU strategies for low-emission mobility, policy makers must consider the behavioural factors of buyers. This study aims to cover this gap by investigating the relation between the national cultural dimensions (Hofstede model) and car adoption by fuel type in EU countries. This could help car sellers to find better solutions for advertising cars with medium and low greenhouse gas emissions. To find better ways to increase the usage of medium- and low-emission cars using targeted advertising, correlations and a multiple regression analysis were used. The results show that the consumer preference for one type of fuel is correlated with at least one of Hofstede’s six cultural dimensions: the power distance index; individualism versus collectivism; masculinity versus femininity; the uncertainty avoidance index; long-term orientation versus short-term normative orientation; indulgence versus restraint. The major conclusion of the study underlines that, with increases in the individualism versus collectivism and indulgence versus restraint scores, the usage of low- and medium-emission cars also increases, and with the increase in the power distance and uncertainty avoidance index, the usage of low- and medium emission cars decreases. At the same time, the driving preference for low- and medium-emission vehicles decreases with the tendency towards collectivism and restraint of EU countries.

Designing an Embedded Feature Selection Algorithm for a Drowsiness Detector Model Based on Electroencephalogram Data.

Driver fatigue reduces the safety of traditional driving and limits the widespread adoption of self-driving cars; hence, the monitoring and early detection of drivers' drowsiness plays a key role in driving automation. When representing the drowsiness indicators as large feature vectors, fitting a machine learning model to the problem becomes challenging, and the problem's perspicuity decreases, making dimensionality reduction crucial in practice. For this reason, we propose an embedded feature selection algorithm that can be later utilized as a building block in the system development of a neural network-based drowsiness detector. We have adopted a technique: a so-called Feature Prune Layer is placed in front of the first layer in the architecture; as a result, its weights change regarding the importance of the corresponding input features and are deleted iteratively until the desired number is reached. We test the algorithm on EEG data, as it is one of the best indicators of drowsiness based on the literature. The proposed FS algorithm is able to reduce the original feature set by 95% with only 1% degradation in precision, while the precision increases by 1.5% and 2.7% respectively when selecting the top 10% and top 20% of the initial features. Moreover, the proposed method outperforms the widely popular Principal Component Analysis and the Chi-squared test when reducing the original feature set by 95%: it achieves 24.3% and 3.2% higher precision respectively.

Fear of AI: an inquiry into the adoption of autonomous cars in spite of fear, and a theoretical framework for the study of artificial intelligence technology acceptance

Artificial intelligence (AI) is becoming part of the everyday. During this transition, people’s intention to use AI technologies is still unclear and emotions such as fear are influencing it. In this paper, we focus on autonomous cars to first verify empirically the extent to which people fear AI and then examine the impact that fear has on their intention to use AI-driven vehicles. Our research is based on a systematic survey and it reveals that while individuals are largely afraid of cars that are driven by AI, they are nonetheless willing to adopt this technology as soon as possible. To explain this tension, we extend our analysis beyond just fear and show that people also believe that AI-driven cars will generate many individual, urban and global benefits. Subsequently, we employ our empirical findings as the foundations of a theoretical framework meant to illustrate the main factors that people ponder when they consider the use of AI tech. In addition to offering a comprehensive theoretical framework for the study of AI technology acceptance, this paper provides a nuanced understanding of the tension that exists between the fear and adoption of AI, capturing what exactly people fear and intend to do.

Effect of Cyber Vulnerabilities on the Adoption of Self-Driving Vehicles – A Review

One of the leading disruptive technologies in the upcoming technological revolution is Self-Driving vehicles. However, the absence of security is the greatest obstacle to adoption. This study looks at how cybersecurity impacts the adoption of driverless cars. The purpose of this paper is to perform a literature review supporting the in-depth analysis of cybersecurity and its impacts on the slower adoption rate of Self-Driving Vehicles. The study's primary goal is to determine the connection between worries about cybersecurity and the rate of adoption of self-driving vehicles. Driverless vehicles are the most effective and cutting-edge technology in the transportation sector, yet there are barriers to their widespread adoption because of cybersecurity worries. As a result, this study will clarify the cybersecurity issues that contributed to the slower deployment of autonomous vehicles. The NIST Cybersecurity Framework serves as the study's theoretical foundation. This paradigm consistently identifies the barriers to new technology adoption in cybersecurity.

Adoption of Chinese Cars and the Shift to Electric Vehicles: Early Evidence From Norway

Adoption of Chinese Cars and the Shift to Electric Vehicles: Early Evidence From Norway

Under-Estimated Factors in the Adoption of Self-Driving Cars

Under-Estimated Factors in the Adoption of Self-Driving Cars

ЕЛЕКТРОМОБІЛІЗАЦІЯ В ЗАХІДНІЙ ЄВРОПІ: ПРИЧИНИ ТА НАСЛІДКИ ЗАЛЕЖНО ВІД ПОЛІТИКИ СТИМУЛЮВАННЯ РІЗНИХ КРАЇН

Introduction. This article is part of a study of the trend of unconditional change in the automotive industry and part of the electric power industry, which is currently taking place and will transform the world economy in the future. It concerns the transition of the market of cars with internal combustion engines to cars with electric motors and battery power systems (BEVs). The article examines the reasons for the transition of the global and European automotive industry to electric vehicles, the dynamics of this process in recent years, as well as methods of stimulating the transition to BEVs in different European countries, including tax incentives and other means of stimulating the purchase of environmentally friendly cars. Objective. To determine the most effective methods of stimulating the electric car market to replicate the positive experience in Ukraine. Research methods. To study the effectiveness of various methods of stimulating demand for electric vehicles, the main incentives (tax breaks, reduction of certain types of fees, compensation for the purchase of BEVs and electric charging stations, cashback, etc.) in different European countries (EU and EFTA) were analyzed. These incentives were compared with the existing results of the electric vehicle market in recent years and the nearest reporting period of the year. Conclusions on the effectiveness of the incentives were drawn. Results. The analysis showed that the best rates of electric car adoption in the country were achieved by the most economically developed countries in Europe, which are located in the north or west of the continent. In addition, the market share of electric vehicles compared to traditional cars with internal combustion engines is higher where incentives have been introduced for a long time, for ten years or more. These incentives were tangible for consumers who chose cars with different drive systems. Prospects for further research. The trend of switching to environmentally friendly vehicles is inevitable for Europe and Ukraine. Based on the experience of introducing electromobilization in developed European countries, it is necessary to create our own system of incentives and benefits for the transition to electric vehicles, to determine the priority areas of the industry.

With Great Power (Prices) Comes Great Tail Pipe Emissions? A Natural Experiment of Electricity Prices and Electric Car Adoption.

With Great Power (Prices) Comes Great Tail Pipe Emissions? A Natural Experiment of Electricity Prices and Electric Car Adoption.

Influence of government financial incentives on electric car adoption: empirical evidence from India

PurposeElectric cars have very little market share in developing countries despite their environmental benefits. Thus, governments have started promoting electric cars by providing financial incentives to consumers. The current article aims to examine the direct and indirect effects of government financial incentives on consumer electric car adoption in India.Design/methodology/approachThe study followed a quantitative research method that employed a self-administered survey questionnaire. Structural Equation Modelling and Multi-Group Analysis were followed for data analysis.FindingsThe study revealed that financial incentives have an indirect effect on electric car adoption intention rather than a direct effect. In addition, financial incentives were found to have a direct effect on attitude and Perceived Behavioural Control (PBC). Attitude and PBC positively influenced consumer adoption intention.Practical implicationsThe insights and implications from the present study would help policymakers and marketers to formulate better incentive policies and market strategies to increase consumer acceptance of electric cars in developing countries.Originality/valueThe study contributes to the literature by analysing the underlying mechanism that links government financial incentives to electric car adoption intention. This study also explored the direct effect of financial incentives on attitude and PBC, which are less investigated in electric vehicle literature. In addition, the present article also assessed the moderating role of age in electric car adoption, which has mixed evidence in the literature, and such studies are scarce in the Indian context.

A factor structure for adoption of hybrid vehicles: Differing impact on males, females and different age groups

This study assessed the validity and reliability of the scales (latent factors) that can help measure consumers' actual adoption of hybrid cars. In so doing, it verified the factor structure of a set of measured variables. It then investigated the impact of each scales' measured variables on males, females and different age groups. The findings of prior studies, even the most recent ones, are inconclusive when it comes to the perceptions of consumers of different age group and gender towards purchase of such vehicles. This study adopted a quantitative approach of self-administered online survey, analyzing the data using SPSS and AMOS statistical tools. A conceptual framework was developed, incorporating the green self-image variable of the Self-image Congruence theory to the variables of price and cost savings, knowledge, hedonic and symbolic attributes, subjective norms, and time (as a risk factor). Confirmatory factor analysis results reported subjective norms as largely impacting males, females as well as all age groups while the rest had varied effects, with the impact of hedonic and symbolic attributes not as strong as the rest, except for on females. The empirical evidence confirms the suitability of most of the measured variables of the latent factors. Future researchers can use the same measured variables of these latent factors to further this research by investigating how these influence the actual adoption of hybrid vehicles, using a structural equation model. The six variables have not been tested together, in the same manner in any other prior study, to the best of the researchers' knowledge. In addition, this study presents a number of useful practical implications and recommendations based on the impacts of each measured variable.